Ottakath Najmath, Elharrouss Omar, Almaadeed Noor, Al-Maadeed Somaya, Mohamed Amr, Khattab Tamer, Abualsaud Khalid
Qatar University, College of Engineering, Department of Computer Science and Engineering, Qatar.
Qatar University, College of Engineering, Department of Electrical Engineering, Qatar.
Displays. 2022 Jul;73:102235. doi: 10.1016/j.displa.2022.102235. Epub 2022 May 10.
The COVID-19 outbreak has extenuated the need for a monitoring system that can monitor face mask adherence and social distancing with the use of AI. With the existing video surveillance systems as base, a deep learning model is proposed for mask detection and social distance measurement. State-of-the-art object detection and recognition models such as Mask RCNN, YOLOv4, YOLOv5, and YOLOR were trained for mask detection and evaluated on the existing datasets and on a newly proposed video mask detection dataset the ViDMASK. The obtained results achieved a comparatively high mean average precision of 92.4% for YOLOR. After mask detection, the distance between people's faces is measured for high risk and low risk distance. Furthermore, the new large-scale mask dataset from videos named ViDMASK diversifies the subjects in terms of pose, environment, quality of image, and versatile subject characteristics, producing a challenging dataset. The tested models succeed in detecting the face masks with high performance on the existing dataset, MOXA. However, with the VIDMASK dataset, the performance of most models are less accurate because of the complexity of the dataset and the number of people in each scene. The link to ViDMask dataset and the base codes are available at https://github.com/ViDMask/VidMask-code.git.
新冠疫情加剧了对一种能够利用人工智能监测口罩佩戴情况和社交距离的监测系统的需求。以现有的视频监控系统为基础，提出了一种用于口罩检测和社交距离测量的深度学习模型。诸如Mask RCNN、YOLOv4、YOLOv5和YOLOR等先进的目标检测和识别模型被用于口罩检测训练，并在现有数据集以及新提出的视频口罩检测数据集ViDMASK上进行评估。对于YOLOR，所获得的结果实现了相对较高的92.4%的平均精度均值。在口罩检测之后，测量人们面部之间的距离以确定高风险和低风险距离。此外，新的来自视频的大规模口罩数据集ViDMASK在姿势、环境、图像质量和通用主体特征方面使主体多样化，产生了一个具有挑战性的数据集。所测试的模型在现有数据集MOXA上成功地以高性能检测出口罩。然而，对于VIDMASK数据集，由于数据集的复杂性和每个场景中的人数，大多数模型的性能不太准确。ViDMask数据集和基础代码的链接可在https://github.com/ViDMask/VidMask-code.git获取。
